This invention relates to the field of biometric matching. In particular, the invention relates to providing two-directional biometric matching.
Biometric systems identify people by their physical characteristics. Biometric identifiers are distinctive, measurable characteristics used to describe individuals.
There are two main types of applications of biometric identification. A first application is for identification of an individual. A biometric system performs a one-to-many comparison against a database of existing biometric data. The system attempts to match the new biometric sample to stored biometric records of known individuals. A successful identification is achieved if the comparison of the biometric sample to a record in the database falls within a previously set threshold.
The second application of biometric identification is for authentication or verification of an individual, also known as one-to-one matching. In this case, the biometric system performs a comparison of a biometric input to a single biometric record in order to verify that the individual is the person they claim to be. The biometric record may be generated during an enrolment or registration process.
The most common forms of biometrics used in automated systems include: fingerprint, face, voice, iris, signature, hand geometry, vein patterns, etc. Biometric data is generated by acquiring the biometric information using a sensor and refining the information as a record or template in the form of an image, signal or data.
Biometric matching systems provide the capability to match two biometric records of biometric characteristics and provide a score based on their degree of similarity. Two records from the same person should have a high degree of similarity and score highly while two records from different individuals should have a lower score and score low.
Each record is, however, captured under different conditions and also different individuals have characteristics that vary statistically. The outcome of this is that when two individual records of characteristics are matched against a large background population (100's of millions for a national ID scheme) gathered using different equipment and environmental conditions, a whole range of scores is seen. These scores tend to exhibit a ‘normal’ distribution around a matched and non-matched score such that there is a region of overlap.
Systems are tuned to set a threshold score, which determines a ‘match’ decision. This does mean that there are some ‘false matches’ and ‘false non-matches’ present in the results of their operation around this threshold score. Current systems cannot however determine which individual pairs are potentially false declarations.
The aim of biometric matching system vendors is to produce a system that minimizes the populations of ‘false matches’ and ‘false non-matches’ present in the results of their operation. This is an active research topic and results in matching algorithms that are more complex and layered in their operation.
Therefore, there is a need in the art to address the aforementioned problems.